10.1. Operating System Interface¶

The os module provides dozens of functions for interacting with the
operating system:

>>> import os
>>> os.getcwd()      # Return the current working directory
'C:\\Python311'
>>> os.chdir('/server/accesslogs')   # Change current working directory
>>> os.system('mkdir today')   # Run the command mkdir in the system shell
0

Be sure to use the import os style instead of from os import * . This
will keep os.open() from shadowing the built-in open() function which
operates much differently.

The built-in dir() and help() functions are useful as interactive
aids for working with large modules like os :

>>> import os
>>> dir(os)
<returns a list of all module functions>
>>> help(os)
<returns an extensive manual page created from the module's docstrings>

For daily file and directory management tasks, the shutil module provides
a higher level interface that is easier to use:

>>> import shutil
>>> shutil.copyfile('data.db', 'archive.db')
'archive.db'
>>> shutil.move('/build/executables', 'installdir')
'installdir'

10.2. File Wildcards¶

The glob module provides a function for making file lists from directory
wildcard searches:

>>> import glob
>>> glob.glob('*.py')
['primes.py', 'random.py', 'quote.py']

10.3. Command Line Arguments¶

Common utility scripts often need to process command line arguments. These
arguments are stored in the sys module’s argv attribute as a list. For
instance the following output results from running python demo.py one two
three at the command line:

>>> import sys
>>> print(sys.argv)
['demo.py', 'one', 'two', 'three']

The argparse module provides a more sophisticated mechanism to process
command line arguments. The following script extracts one or more filenames
and an optional number of lines to be displayed:

import argparse

parser = argparse.ArgumentParser(
    prog='top',
    description='Show top lines from each file')
parser.add_argument('filenames', nargs='+')
parser.add_argument('-l', '--lines', type=int, default=10)
args = parser.parse_args()
print(args)

When run at the command line with python top.py --lines=5 alpha.txt
beta.txt , the script sets args.lines to 5 and args.filenames
to ['alpha.txt', 'beta.txt'] .

10.4. Error Output Redirection and Program Termination¶

The sys module also has attributes for stdin , stdout , and stderr .
The latter is useful for emitting warnings and error messages to make them
visible even when stdout has been redirected:

>>> sys.stderr.write('Warning, log file not found starting a new one\n')
Warning, log file not found starting a new one

The most direct way to terminate a script is to use sys.exit() .

10.5. String Pattern Matching¶

The re module provides regular expression tools for advanced string
processing. For complex matching and manipulation, regular expressions offer
succinct, optimized solutions:

>>> import re
>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
['foot', 'fell', 'fastest']
>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
'cat in the hat'

When only simple capabilities are needed, string methods are preferred because
they are easier to read and debug:

>>> 'tea for too'.replace('too', 'two')
'tea for two'

10.6. Mathematics¶

The math module gives access to the underlying C library functions for
floating point math:

>>> import math
>>> math.cos(math.pi / 4)
0.70710678118654757
>>> math.log(1024, 2)
10.0

The random module provides tools for making random selections:

>>> import random
>>> random.choice(['apple', 'pear', 'banana'])
'apple'
>>> random.sample(range(100), 10)   # sampling without replacement
[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
>>> random.random()    # random float
0.17970987693706186
>>> random.randrange(6)    # random integer chosen from range(6)
4

The statistics module calculates basic statistical properties
(the mean, median, variance, etc.) of numeric data:

>>> import statistics
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> statistics.mean(data)
1.6071428571428572
>>> statistics.median(data)
1.25
>>> statistics.variance(data)
1.3720238095238095

The SciPy project <https://scipy.org> has many other modules for numerical
computations.

10.7. Internet Access¶

There are a number of modules for accessing the internet and processing internet
protocols. Two of the simplest are urllib.request for retrieving data
from URLs and smtplib for sending mail:

>>> from urllib.request import urlopen
>>> with urlopen('http://worldtimeapi.org/api/timezone/etc/UTC.txt') as response:
...     for line in response:
...         line = line.decode()             # Convert bytes to a str
...         if line.startswith('datetime'):
...             print(line.rstrip())         # Remove trailing newline
...
datetime: 2022-01-01T01:36:47.689215+00:00

>>> import smtplib
>>> server = smtplib.SMTP('localhost')
>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
... """To: jcaesar@example.org
... From: soothsayer@example.org
...
... Beware the Ides of March.
... """)
>>> server.quit()

(Note that the second example needs a mailserver running on localhost.)

10.8. Dates and Times¶

The datetime module supplies classes for manipulating dates and times in
both simple and complex ways. While date and time arithmetic is supported, the
focus of the implementation is on efficient member extraction for output
formatting and manipulation. The module also supports objects that are timezone
aware.

>>> # dates are easily constructed and formatted
>>> from datetime import date
>>> now = date.today()
>>> now
datetime.date(2003, 12, 2)
>>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'

>>> # dates support calendar arithmetic
>>> birthday = date(1964, 7, 31)
>>> age = now - birthday
>>> age.days
14368

10.9. Data Compression¶

Common data archiving and compression formats are directly supported by modules
including: zlib , gzip , bz2 , lzma , zipfile and
tarfile .

>>> import zlib
>>> s = b'witch which has which witches wrist watch'
>>> len(s)
41
>>> t = zlib.compress(s)
>>> len(t)
37
>>> zlib.decompress(t)
b'witch which has which witches wrist watch'
>>> zlib.crc32(s)
226805979

10.10. Performance Measurement¶

Some Python users develop a deep interest in knowing the relative performance of
different approaches to the same problem. Python provides a measurement tool
that answers those questions immediately.

For example, it may be tempting to use the tuple packing and unpacking feature
instead of the traditional approach to swapping arguments. The timeit
module quickly demonstrates a modest performance advantage:

>>> from timeit import Timer
>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
0.57535828626024577
>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
0.54962537085770791

In contrast to timeit ’s fine level of granularity, the profile and
pstats modules provide tools for identifying time critical sections in
larger blocks of code.

10.11. Quality Control¶

One approach for developing high quality software is to write tests for each
function as it is developed and to run those tests frequently during the
development process.

The doctest module provides a tool for scanning a module and validating
tests embedded in a program’s docstrings. Test construction is as simple as
cutting-and-pasting a typical call along with its results into the docstring.
This improves the documentation by providing the user with an example and it
allows the doctest module to make sure the code remains true to the
documentation:

def average(values):
    """Computes the arithmetic mean of a list of numbers.

    >>> print(average([20, 30, 70]))
    40.0
    """
    return sum(values) / len(values)

import doctest
doctest.testmod()   # automatically validate the embedded tests

The unittest module is not as effortless as the doctest module,
but it allows a more comprehensive set of tests to be maintained in a separate
file:

import unittest

class TestStatisticalFunctions(unittest.TestCase):

    def test_average(self):
        self.assertEqual(average([20, 30, 70]), 40.0)
        self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
        with self.assertRaises(ZeroDivisionError):
            average([])
        with self.assertRaises(TypeError):
            average(20, 30, 70)

unittest.main()  # Calling from the command line invokes all tests

10.12. Batteries Included¶

Python has a “batteries included” philosophy. This is best seen through the
sophisticated and robust capabilities of its larger packages. For example:

• The xmlrpc.client and xmlrpc.server modules make implementing
  remote procedure calls into an almost trivial task. Despite the modules’
  names, no direct knowledge or handling of XML is needed.




• The email package is a library for managing email messages, including
  MIME and other RFC 2822 -based message documents. Unlike smtplib and
  poplib which actually send and receive messages, the email package has
  a complete toolset for building or decoding complex message structures
  (including attachments) and for implementing internet encoding and header
  protocols.




• The json package provides robust support for parsing this
  popular data interchange format. The csv module supports
  direct reading and writing of files in Comma-Separated Value format,
  commonly supported by databases and spreadsheets. XML processing is
  supported by the xml.etree.ElementTree , xml.dom and
  xml.sax packages. Together, these modules and packages
  greatly simplify data interchange between Python applications and
  other tools.




• The sqlite3 module is a wrapper for the SQLite database
  library, providing a persistent database that can be updated and
  accessed using slightly nonstandard SQL syntax.




• Internationalization is supported by a number of modules including
  gettext , locale , and the codecs package.